ArticleScientific reports2019
Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Discrepant treponemal test results: Identification of associated risk factors through machine learning technology in 18-year electronic medical records and national claims data.Biomedical journal · 2026Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.Scientific reports · 2024Article
- Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.Journal of medical Internet research · 2022Article
- Machine learning and applications in microbiology.FEMS microbiology reviews · 2021Review
- Review
- Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Combination of RERG and ZNF671 methylation rates in circulating cell-free DNA: A novel biomarker for screening of nasopharyngeal carcinoma.Cancer science · 2020Article
- Improving Multi-Tumor Biomarker Health Check-up Tests with Machine Learning Algorithms.Cancers · 2020Article
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Authors and funding
11 authors.
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Abstract
Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009-2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.
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